PMID- 32225061
OWN - NLM
STAT- MEDLINE
DCOM- 20201222
LR  - 20201222
IS  - 1420-3049 (Electronic)
IS  - 1420-3049 (Linking)
VI  - 25
IP  - 7
DP  - 2020 Mar 26
TI  - Feedforward Artificial Neural Network-Based Model for Predicting the Removal of 
      Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs.
LID - 10.3390/molecules25071511 [doi]
LID - 1511
AB  - In the recent decade, deep eutectic solvents (DESs) have occupied a strategic 
      place in green chemistry research. This paper discusses the application of DESs 
      as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce 
      novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous 
      solution. Also, it focuses on the application of the feedforward backpropagation 
      neural network (FBPNN) technique to predict the adsorption capacity of 
      DES-functionalized CNTs. The optimum adsorption conditions that are required for 
      the maximum removal of 2,4-DCP were determined by studying the impact of the 
      operational parameters (i.e., the solution pH, adsorbent dosage, and contact 
      time) on the adsorption capacity of the produced adsorbents. Two kinetic models 
      were applied to describe the adsorption rate and mechanism. Based on the 
      correlation coefficient (R(2)) value, the adsorption kinetic data were well 
      defined by the pseudo second-order model. The precision and efficiency of the 
      FBPNN model was approved by calculating four statistical indicators, with the 
      smallest value of the mean square error being 5.01 x 10(-5). Moreover, further 
      accuracy checking was implemented through the sensitivity study of the 
      experimental parameters. The competence of the model for prediction of 2,4-DCP 
      removal was confirmed with an R(2) of 0.99.
FAU - Ibrahim, Rusul Khaleel
AU  - Ibrahim RK
AD  - Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala 
      Lumpur 50603, Malaysia.
FAU - Fiyadh, Seef Saadi
AU  - Fiyadh SS
AD  - Nanotechnology & Catalysis Research Centre, University of Malaya, Kuala Lumpur 
      50603, Malaysia.
FAU - AlSaadi, Mohammed Abdulhakim
AU  - AlSaadi MA
AUID- ORCID: 0000-0001-9278-6490
AD  - Department of Materials Science and Metallurgy, University of Nizwa, Birkat Al 
      Mawz 616, Oman.
AD  - Department of Civil Engineering, Al-Maarif University College, Ramadi 31001, 
      Iraq.
FAU - Hin, Lai Sai
AU  - Hin LS
AD  - Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala 
      Lumpur 50603, Malaysia.
FAU - Mohd, Nuruol Syuhadaa
AU  - Mohd NS
AD  - Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala 
      Lumpur 50603, Malaysia.
FAU - Ibrahim, Shaliza
AU  - Ibrahim S
AD  - Institute of Ocean and Earth Sciences (IOES), University of Malaya, Kuala Lumpur 
      50603, Malaysia.
FAU - Afan, Haitham Abdulmohsin
AU  - Afan HA
AUID- ORCID: 0000-0002-4957-756X
AD  - Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala 
      Lumpur 50603, Malaysia.
FAU - Fai, Chow Ming
AU  - Fai CM
AD  - Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), 
      Selangor 43000, Malaysia.
FAU - Ahmed, Ali Najah
AU  - Ahmed AN
AUID- ORCID: 0000-0002-5618-6663
AD  - Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 
      Selangor 43000, Malaysia.
FAU - Elshafie, Ahmed
AU  - Elshafie A
AUID- ORCID: 0000-0001-5018-8505
AD  - Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala 
      Lumpur 50603, Malaysia.
LA  - eng
GR  - UMRG-RP025C-18SUS/Universiti Malaya/
GR  - RJO 10436494/Universiti Tenaga Nasional/
PT  - Journal Article
DEP - 20200326
PL  - Switzerland
TA  - Molecules
JT  - Molecules (Basel, Switzerland)
JID - 100964009
RN  - 0 (Nanotubes, Carbon)
RN  - 0 (Phenols)
RN  - 0 (Solvents)
RN  - 0 (Water Pollutants, Chemical)
SB  - IM
MH  - Adsorption
MH  - Algorithms
MH  - Kinetics
MH  - Models, Theoretical
MH  - Nanotubes, Carbon/*chemistry
MH  - *Neural Networks, Computer
MH  - Phenols/*chemistry
MH  - Solvents/*chemistry
MH  - Water Pollutants, Chemical/*chemistry
MH  - Water Purification
PMC - PMC7180483
OTO - NOTNLM
OT  - adsorption
OT  - carbon nanotubes
OT  - deep eutectic solvents
OT  - feedforward back propagation neural network
OT  - water quality
COIS- The authors declare no conflict of interest.
EDAT- 2020/04/01 06:00
MHDA- 2020/12/23 06:00
PMCR- 2020/03/26
CRDT- 2020/04/01 06:00
PHST- 2019/07/09 00:00 [received]
PHST- 2019/08/07 00:00 [revised]
PHST- 2019/08/25 00:00 [accepted]
PHST- 2020/04/01 06:00 [entrez]
PHST- 2020/04/01 06:00 [pubmed]
PHST- 2020/12/23 06:00 [medline]
PHST- 2020/03/26 00:00 [pmc-release]
AID - molecules25071511 [pii]
AID - molecules-25-01511 [pii]
AID - 10.3390/molecules25071511 [doi]
PST - epublish
SO  - Molecules. 2020 Mar 26;25(7):1511. doi: 10.3390/molecules25071511.